{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-28T01:07:36.475995Z",
     "start_time": "2025-09-28T01:07:36.468996Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import precision_score, recall_score"
   ],
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T01:00:57.410116Z",
     "start_time": "2025-09-28T01:00:57.302034Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data = pd.read_csv('../../../dataset/银行贷款违约状态数据集.csv', encoding='gbk')\n",
    "print(data.columns)"
   ],
   "id": "22b727809ae6cfd4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['编号', '贷款金额', '已发放金额', '期限', '利率', '房产状态', '贷借收入比例', '两年逾期次数',\n",
      "       '六个月逾期次数', '开户记录', '总账户数', '已还款总额', '已收到滞纳金总额', '状态（0：不会逾期，1：会逾期）'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T01:00:57.456526Z",
     "start_time": "2025-09-28T01:00:57.443142Z"
    }
   },
   "cell_type": "code",
   "source": "data",
   "id": "cc66046eae779f43",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             编号   贷款金额  已发放金额  期限         利率 房产状态     贷借收入比例  两年逾期次数  六个月逾期次数  \\\n",
       "0      65087372  10000  32236  59  11.135007   按揭  16.284758       1        0   \n",
       "1       1450153   3609  11940  59  12.237563   租赁  15.412409       0        0   \n",
       "2       1969101  28276   9311  59  12.545884   按揭  28.137619       0        0   \n",
       "3       6651430  11170   6954  59  16.731201   按揭  18.043730       1        0   \n",
       "4      14354669  16890  13226  59  15.008300   按揭  17.209886       1        3   \n",
       "...         ...    ...    ...  ..        ...  ...        ...     ...      ...   \n",
       "67458  16164945  13601   6848  59   9.408858   按揭  28.105127       1        0   \n",
       "67459  35182714   8323  11046  59   9.972104   租赁  17.694279       0        0   \n",
       "67460  16435904  15897  32921  59  19.650943   按揭  10.295774       0        0   \n",
       "67461   5300325  16567   4975  59  13.169095   自建   7.614624       0        0   \n",
       "67462  65443173  15353  29875  59  16.034631   按揭  16.052112       0        0   \n",
       "\n",
       "       开户记录  总账户数        已还款总额   已收到滞纳金总额  状态（0：不会逾期，1：会逾期）  \n",
       "0        13     7  2929.646315   0.102055                 0  \n",
       "1        12    13   772.769385   0.036181                 0  \n",
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       "4        13    22   129.239553  19.306646                 0  \n",
       "...     ...   ...          ...        ...               ...  \n",
       "67458    13    19  1978.945960   0.023478                 1  \n",
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       "67461    14    15  3659.334202   0.074508                 0  \n",
       "67462    30    16  1324.255922   0.000671                 0  \n",
       "\n",
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       "      <th>编号</th>\n",
       "      <th>贷款金额</th>\n",
       "      <th>已发放金额</th>\n",
       "      <th>期限</th>\n",
       "      <th>利率</th>\n",
       "      <th>房产状态</th>\n",
       "      <th>贷借收入比例</th>\n",
       "      <th>两年逾期次数</th>\n",
       "      <th>六个月逾期次数</th>\n",
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       "      <th>已还款总额</th>\n",
       "      <th>已收到滞纳金总额</th>\n",
       "      <th>状态（0：不会逾期，1：会逾期）</th>\n",
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       "      <td>租赁</td>\n",
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       "      <td>19.650943</td>\n",
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       "<p>67463 rows × 14 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T01:13:29.788168Z",
     "start_time": "2025-09-28T01:13:28.520561Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#问题四\n",
    "import winsound  # 用于蜂鸣器报警# 计算准确率\n",
    "# 定义特征和目标变量\n",
    "features = ['贷借收入比例', '两年逾期次数', '六个月逾期次数', '利率', '已还款总额']\n",
    "x = data[features]  # 特征\n",
    "y = data['状态（0：不会逾期，1：会逾期）']  # 目标变量\n",
    "\n",
    "# 划分训练集和测试集（测试集占比20%）\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)\n",
    "# 创建逻辑回归模型\n",
    "log_reg = LogisticRegression(class_weight='balanced',random_state=42, max_iter=1000)\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "#在测试机上进行预测\n",
    "y_pred = log_reg.predict(X_test)\n",
    "\n",
    "# 计算评估指标\n",
    "accuracy = log_reg.score(X_test, y_test)  # 准确率\n",
    "precision = precision_score(y_test, y_pred, average='macro', zero_division=0)  # 精确率\n",
    "recall = recall_score(y_test, y_pred, average='macro', zero_division=0)       # 召回率\n",
    "\n",
    "# 输出结果\n",
    "print(f\"模型准确率: {accuracy:.2%}\")\n",
    "print(f\"模型精确率: {precision:.2%}\")\n",
    "print(f\"模型召回率: {recall:.2%}\")\n",
    "\n",
    "# 如果准确率大于 10%，触发蜂鸣器报警（注意：准确率通常远高于10%，可能是你笔误？）\n",
    "if accuracy > 0.1:\n",
    "    print(\"准确率超过10%，触发报警！\")\n",
    "    winsound.Beep(1000, 1000)  # 频率1000Hz，持续1秒\n",
    "else:\n",
    "    print(\"准确率未超过10%，无需报警\")"
   ],
   "id": "c39e4bc6abc28baf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型准确率: 60.31%\n",
      "模型精确率: 50.41%\n",
      "模型召回率: 51.14%\n",
      "准确率超过10%，触发报警！\n"
     ]
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T01:01:18.002703Z",
     "start_time": "2025-09-28T01:00:58.740714Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#问题一、二\n",
    "import tkinter as tk\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 全局变量存储预测结果\n",
    "prediction_result = None\n",
    "# 示例数据加载和模型训练（替换为你的实际数据）\n",
    "def load_data_and_train_model():\n",
    "    # 示例数据路径（替换为你的实际路径）\n",
    "    data = pd.read_csv('../../../dataset/银行贷款违约状态数据集.csv', encoding='gbk')\n",
    "\n",
    "    # 选择特征和目标变量\n",
    "    features = ['贷借收入比例', '两年逾期次数', '六个月逾期次数', '利率', '已还款总额']\n",
    "    X = data[features]\n",
    "    y = data['状态（0：不会逾期，1：会逾期）']\n",
    "    # 标准化数据\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "    # 训练模型\n",
    "    model = LogisticRegression(max_iter=1000)\n",
    "    model.fit(X_scaled, y)\n",
    "\n",
    "    return model, scaler, features\n",
    "\n",
    "# 加载模型和数据\n",
    "model, scaler, feature_names = load_data_and_train_model()\n",
    "\n",
    "# 创建主窗口\n",
    "root = tk.Tk()\n",
    "root.title(\"银行贷款违约预测系统\")\n",
    "\n",
    "# 设置窗口大小\n",
    "root.geometry(\"500x400\")\n",
    "\n",
    "# 添加标题\n",
    "title_label = tk.Label(root, text=\"银行贷款违约预测\", font=(\"Arial\", 16))\n",
    "title_label.pack(pady=10)\n",
    "\n",
    "# 创建输入框和标签\n",
    "input_frame = tk.Frame(root)\n",
    "input_frame.pack(pady=20)\n",
    "\n",
    "inputs = {}\n",
    "for i, feature in enumerate(feature_names):\n",
    "    label = tk.Label(input_frame, text=f\"{feature}:\")\n",
    "    label.grid(row=i, column=0, padx=5, pady=5, sticky=\"e\")\n",
    "\n",
    "    entry = tk.Entry(input_frame)\n",
    "    entry.grid(row=i, column=1, padx=5, pady=5)\n",
    "    inputs[feature] = entry\n",
    "\n",
    "# 预测函数\n",
    "def predict():\n",
    "    global prediction_result\n",
    "    try:\n",
    "        # 获取用户输入\n",
    "        input_values = []\n",
    "        input_data = {}\n",
    "        for feature in feature_names:\n",
    "            value = float(inputs[feature].get())\n",
    "            input_values.append(value)\n",
    "            input_data[feature] = value\n",
    "\n",
    "        # 转换为DataFrame并标准化\n",
    "        input_df = pd.DataFrame([input_values], columns=feature_names)\n",
    "        input_scaled = scaler.transform(input_df)\n",
    "\n",
    "        # 预测\n",
    "        prediction = model.predict(input_scaled)[0]\n",
    "        proba = model.predict_proba(input_scaled)[0]\n",
    "\n",
    "        # 存储预测结果到全局变量\n",
    "        prediction_result = {\n",
    "            \"input_data\": input_data,\n",
    "            \"prediction\": \"会逾期\" if prediction == 1 else \"不会逾期\",\n",
    "            \"confidence\": proba[1] if prediction == 1 else proba[0]\n",
    "        }\n",
    "\n",
    "        # 显示结果\n",
    "        result_label.config(text=f\"预测结果: {prediction_result['prediction']} (置信度: {prediction_result['confidence']:.2%})\")\n",
    "\n",
    "        # 打印输入值和预测结果到控制台\n",
    "        print(\"\\n输入值:\")\n",
    "        for feature, value in input_data.items():\n",
    "            print(f\"{feature}: {value}\")\n",
    "        print(f\"预测结果: {prediction_result['prediction']}\")\n",
    "        print(f\"置信度: {prediction_result['confidence']:.2%}\")\n",
    "\n",
    "    except ValueError:\n",
    "        result_label.config(text=\"请输入有效的数字！\")\n",
    "        print(\"错误：请输入有效的数字！\")\n",
    "\n",
    "# 预测按钮\n",
    "predict_button = tk.Button(root, text=\"预测\", command=predict, bg=\"#4CAF50\", fg=\"white\")\n",
    "predict_button.pack(pady=10)\n",
    "\n",
    "# 结果显示\n",
    "result_label = tk.Label(root, text=\"预测结果: \", font=(\"Arial\", 12))\n",
    "result_label.pack(pady=20)\n",
    "\n",
    "# 运行主循环\n",
    "root.mainloop()"
   ],
   "id": "7b137704139706e6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "输入值:\n",
      "贷借收入比例: 32.232\n",
      "两年逾期次数: 1.0\n",
      "六个月逾期次数: 1.0\n",
      "利率: 31.53\n",
      "已还款总额: 3134.0\n",
      "预测结果: 不会逾期\n",
      "置信度: 90.12%\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T01:01:18.656650Z",
     "start_time": "2025-09-28T01:01:18.238185Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#功能2：使用pyttsx3模块进行语音播报\n",
    "import pyttsx3\n",
    "engine = pyttsx3.init()\n",
    "engine.say(f\"预测的心理健康状态是{prediction_result['prediction']}\")\n",
    "print(f\"预测的心理健康状态是{prediction_result['prediction']}\")\n",
    "engine.runAndWait()"
   ],
   "id": "6ce506e2917d29b5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测的心理健康状态是不会逾期\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T01:01:19.045323Z",
     "start_time": "2025-09-28T01:01:18.688974Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# 设置中文字体（解决中文显示问题）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('../../../dataset/银行贷款违约状态数据集.csv', encoding='gbk')\n",
    "\n",
    "# 选择要对比的两列\n",
    "col1 = '贷借收入比例'\n",
    "col2 = '利率'\n",
    "\n",
    "# 创建图形（1行2列布局）\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "# 第一个子图：col1的分布\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(data[col1], bins=30, color='skyblue', edgecolor='black')\n",
    "plt.title(f'{col1}分布', fontsize=14)\n",
    "plt.xlabel(col1, fontsize=12)\n",
    "plt.ylabel('频数', fontsize=12)\n",
    "plt.grid(True, linestyle='--', alpha=0.5)\n",
    "\n",
    "# 第二个子图：col2的分布\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(data[col2], bins=30, color='salmon', edgecolor='black')\n",
    "plt.title(f'{col2}分布', fontsize=14)\n",
    "plt.xlabel(col2, fontsize=12)\n",
    "plt.ylabel('频数', fontsize=12)\n",
    "plt.grid(True, linestyle='--', alpha=0.5)\n",
    "\n",
    "# 调整布局并显示\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "9c2ba027395a414",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-28T01:01:19.092332Z",
     "start_time": "2025-09-28T01:01:19.078323Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "438b8b98b906f430",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
